Eco-driving is designed to reduce fuel consumption and emissions by the improvement of driving behaviors. The objective of this work is to propose some eco-driving behavior suggestions by analyzing the impact of driving behaviors on vehicle emissions. A Portable Emission Measurement System (PEMS) was used to collect emissions and microscopic driving behavior data. Then the multi-dimensional characteristics of driving behaviors and corresponding link-level emission characteristics were quantified. The results show that the vehicle activities change significantly when the road traffic tends to be saturated or forced. The change of driving behavior and associated vehicle activities leads to the significant increase of emission factor in saturated flow and forced flow. Traditional eco-driving advice advocates using the highest gear as much as possible to reduce fuel consumption. But the results indicate that using the highest gear can achieve a savings in CO₂ while at a cost in NO_X. If the time percentage of the highest gear increase by 54%, the CO₂ emission factors will reduce by 22% while NO_X will increase by 14%. Then a total of six factors were extracted to characterize driving behaviors. The statistical analysis shows that not only the driving operational intensity have an impact on the emissions factors but also the durations and frequencies of individual maneuver states will affect emissions. Decision tree algorithm was used to identify eco-driving behaviors. Finally, a model with 97% accuracy was put forward. The conclusions are helpful to improve traditional eco-driving strategies.

生态驾驶旨在通过改善驾驶行为来减少燃油消耗和排放。这项工作的目的是通过分析驾驶行为对车辆排放的影响，提出一些生态驾驶行为建议。便携式排放物测量系统（PEMS）用于收集排放物和微观驾驶行为数据。然后，对驾驶行为的多维特征和相应的链路级排放特征进行了量化。结果表明，当道路交通趋于饱和或被迫行驶时，车辆活动发生显着变化。驾驶行为和相关车辆活动的变化导致饱和流量和强制流量中排放因子的显着增加。传统的生态驾驶建议提倡尽可能使用最高档位以减少油耗。但结果表明，使用最高档位可以节省二氧化碳_2，而以NO _X为代价。如果最高档位的时间百分比增加54％，则CO ₂排放因子将减少22％，而NO _X将增加14％。然后，总共提取了六个因素来表征驾驶行为。统计分析表明，不仅驾驶操作强度会影响排放因子，而且各个操纵状态的持续时间和频率也会影响排放。决策树算法被用来识别生态驾驶行为。最后，提出了具有97％准确性的模型。结论有助于改善传统的生态驾驶策略。